Awesome

Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition

By Hong-Xing Yu*, Michelle Guo*, Alireza Fathi, Yen-Yu Chang, Eric Ryan Chan, Ruohan Gao, Thomas Funkhouser, Jiajun Wu

arXiv link: https://arxiv.org/abs/2303.06138

Setup

git clone https://github.com/michguo/osf.git
cd osf
conda env create -f environment.yml

Data and Models

You can download the data and pretrained models from the following Google Drive links:

• Data (2.0 GB): https://drive.google.com/file/d/1IWNt5R2Mpp1XHo8JiwfHUZXI7Coi4OXO/view?usp=share_link
• Models (0.8 GB): https://drive.google.com/file/d/1oj-F4SWgt-vfRyS9MHIVf9cdJnZe-p81/view?usp=share_link

Training and Evaluation

Configuration files can be found in the configs folder. To train an OSF, run the following command:

python run_osf.py --config ${CONFIG_PATH}

For testing, you can run

python run_osf.py --config ${CONFIG_PATH} --render_only --render_test

Composing OSFs

After training individual OSFs, you can compose them together into arbitrary scene arrangements at test time. This example composes the checkers background and the ObjectFolder objects together into a scene:

python run_osf.py --config=configs/compose.txt

KiloOSF

For the extension of KiloOSF where we distill a trained OSF model to accelerate rendering, please refer to KiloOSF.

Citation

@article{yu2023osf,
    title={Learning Object-centric Neural Scattering Functions for Free-viewpoint Relighting and Scene Composition},
    author={Yu, Hong-Xing and Guo, Michelle and Fathi, Alireza and Chang, Yen-Yu and Chan, Eric Ryan and Gao, Ruohan and Funkhouser, Thomas and Wu, Jiajun},
    journal={Transactions on Machine Learning Research},
    year={2023}
}

Acknowledgements

Our code framework is adapted from nerf-pytorch.